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Georges Aad

Bio: Georges Aad is an academic researcher from Aix-Marseille University. The author has contributed to research in topics: Large Hadron Collider & Higgs boson. The author has an hindex of 135, co-authored 1121 publications receiving 88811 citations. Previous affiliations of Georges Aad include Centre national de la recherche scientifique & University of Udine.


Papers
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Journal ArticleDOI
Georges Aad1, Brad Abbott2, Dale Charles Abbott3, A. Abed Abud4  +2942 moreInstitutions (199)
TL;DR: A search for Higgs boson decays into a Z boson and a light resonance in two-lepton plus jet events is performed, using a pp collision dataset with an integrated luminosity of 139 fb^{-1} collected by the ATLAS experiment at the CERN LHC.
Abstract: A search for Higgs boson decays into a Z boson and a light resonance in two-lepton plus jet events is performed, using a pp collision dataset with an integrated luminosity of 139 fb^{-1} collected at sqrt[s]=13 TeV by the ATLAS experiment at the CERN LHC. The resonance considered is a light boson with a mass below 4 GeV from a possible extended scalar sector or a charmonium state. Multivariate discriminants are used for the event selection and for evaluating the mass of the light resonance. No excess of events above the expected background is found. Observed (expected) 95% confidence-level upper limits are set on the Higgs boson production cross section times branching fraction to a Z boson and the signal resonance, with values in the range 17-340 pb (16_{-5}^{+6}-320_{-90}^{+130} pb) for the different light spin-0 boson mass and branching fraction hypotheses, and with values of 110 and 100 pb (100_{-30}^{+40} and 100_{-30}^{+40} pb) for the η_{c} and J/ψ hypotheses, respectively.

19 citations

Journal ArticleDOI
Georges Aad1, T. Abajyan2, Brad Abbott3, J. Abdallah  +2878 moreInstitutions (179)
TL;DR: In this article, the authors measured the transverse thrust, thrust minor, and transverse sphericity of charged particle collisions at a center-of-mass energy of 7 TeV using the ATLAS detector at the LHC.
Abstract: The measurement of charged-particle event shape variables is presented in inclusive inelastic pp collisions at a center-of-mass energy of 7 TeV using the ATLAS detector at the LHC. The observables studied are the transverse thrust, thrust minor, and transverse sphericity, each defined using the final-state charged particles' momentum components perpendicular to the beam direction. Events with at least six charged particles are selected by a minimum-bias trigger. In addition to the differential distributions, the evolution of each event shape variable as a function of the leading charged-particle transverse momentum, charged-particle multiplicity, and summed transverse momentum is presented. Predictions from several Monte Carlo models show significant deviations from data.

19 citations

Journal ArticleDOI
Georges Aad1, Brad Abbott2, Jalal Abdallah3, Ovsat Abdinov4  +2854 moreInstitutions (210)
TL;DR: In this article, a measurement of the correlations between the polar angles of leptons from the decay of pair-produced t and (t) over bar quarks in the helicity basis is reported, using proton-proton collision data collected by the ATLAS detector at the LHC.
Abstract: A measurement of the correlations between the polar angles of leptons from the decay of pair-produced t and (t) over bar quarks in the helicity basis is reported, using proton-proton collision data collected by the ATLAS detector at the LHC. The dataset corresponds to an integrated luminosity of 4.6 fb(-1) at a center-of-mass energy of root s = 7 TeV collected during 2011. Candidate events are selected in the dilepton topology with large missing transverse momentum and at least two jets. The angles theta(1) and theta(2) between the charged leptons and the direction of motion of the parent quarks in the t (t) over bar rest frame are sensitive to the spin information, and the distribution of cos theta(1).cos theta(2) is sensitive to the spin correlation between the t and (t) over bar quarks. The distribution is unfolded to parton level and compared to the next-to-leading order prediction. A good agreement is observed.

19 citations

Journal ArticleDOI
Morad Aaboud, Georges Aad1, Brad Abbott2, Ovsat Abdinov3  +2883 moreInstitutions (196)
TL;DR: The decay width of the top quark is measured using a template fit to distributions of kinematic observables associated with the hadronically and semileptonically decaying top quarks and is consistent with the prediction of the Standard Model.
Abstract: This paper presents a direct measurement of the decay width of the top quark using t (t) over bar events in the lepton+jets final state. The data sample was collected by the ATLAS detector at the L ...

19 citations

Journal ArticleDOI
Morad Aaboud, Z. Barnovska1, Nicolas Berger2, Marco Delmastro2, L. Di Ciaccio2, Sabine Elles2, Kirill Grevtsov2, Tetiana Hryn'ova2, Stéphane Jézéquel2, I. Koletsou2, Remi Lafaye2, Jessica Levêque2, P. Mastrandea3, G. Sauvage, Emmanuel Sauvan2, O. Simard4, T. Todorov2, T. Todorov5, Isabelle Wingerter-Seez2, E. Yatsenko2, S. Albrand6, Simon Berlendis6, Agni Bethani6, Clement Camincher6, Johann Collot6, Sabine Crépé-Renaudin6, Pierre-Antoine Delsart6, Carolina Gabaldon6, Marie-Hélène Genest6, Poj Gradin6, J-Y. Hostachy6, Fabienne Ledroit-Guillon6, Annick Lleres6, Arnaud Lucotte6, Fairouz Malek6, Elisabeth Petit6, Jan Stark6, Benjamin Trocmé6, M. Wu6, Ghita Rahal, Georges Aad1, Mahmoud Alstaty1, Marlon Barbero1, A. Calandri1, Thomas Philippe Calvet1, Yann Coadou1, Cristinel Diaconu1, Fares Djama1, Venugopal Ellajosyula1, Lorenzo Feligioni1, Asma Hadef1, Gregory David Hallewell1, Fabrice Hubaut1, Sebastien Jonathan Kahn1, E. B. F. G. Knoops1, E. Le Guirriec1, Jiurong Liu1, K. Liu1, D. Madaffari1, Emmanuel Monnier1, S. Muanza1, Elemer Nagy1, Pascal Pralavorio1, Yulia Rodina1, Alexandre Rozanov1, Mossadek Talby1, T. Theveneaux-Pelzer1, R. E. Ticse Torres1, Sylvain Tisserant1, Jozsef Toth1, Francois Touchard1, Laurent Vacavant1, Chunjie Wang7, Djamel Eddine Boumediene8, Emmanuel Busato8, David Calvet8, Samuel Calvet8, Arthur Rene Chomont8, Julien Donini8, Ph Gris8, R. Madar8, Dominique Pallin8, S. M. Romano Saez8, Claudio Santoni8, Dorian Simon8, Francois Vazeille8 
TL;DR: A search for a heavy particle decaying into different flavour dilepton pairs using proton–proton collision data collected by the ATLAS detector at the Large Hadron Collider results are interpreted as limits on the threshold mass for quantum black hole production.
Abstract: A search is performed for a heavy particle decaying into different flavour dilepton pairs ($e\mu$, $e\tau$ or $\mu\tau$), using 3.2 $fb^{-1}$ of proton--proton collision data at $\sqrt{s}=13$ TeV collected in 2015 by the ATLAS detector at the Large Hadron Collider. No excess over the Standard Model prediction is observed. Limits at the 95% credibility level are set on the mass of a $Z^{\prime}$ boson with lepton-flavour-violating couplings at 3.0, 2.7 and 2.6 TeV, and on the mass of a supersymmetric $\tau$ sneutrino with $R$-parity-violating couplings at 2.3, 2.2 and 1.9 TeV, for $e\mu$, $e\tau$ and $\mu\tau$ final states, respectively. The results are also interpreted as limits on the threshold mass for quantum black hole production.

19 citations


Cited by
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Journal ArticleDOI

[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Journal ArticleDOI
01 Apr 1988-Nature
TL;DR: In this paper, a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) is presented.
Abstract: Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

9,929 citations

Journal ArticleDOI
Georges Aad1, T. Abajyan2, Brad Abbott3, Jalal Abdallah4  +2964 moreInstitutions (200)
TL;DR: In this article, a search for the Standard Model Higgs boson in proton-proton collisions with the ATLAS detector at the LHC is presented, which has a significance of 5.9 standard deviations, corresponding to a background fluctuation probability of 1.7×10−9.

9,282 citations